## pval_cutoff: 0.05
## lfc_cutoff: 1
## low_counts_cutoff: 10
General statistics
# Number of samples
length(counts_data)
## [1] 6
# Number of genes
nrow(counts_data)
## [1] 55487
# Total counts
colSums(counts_data)
## SRR13535276 SRR13535278 SRR13535280 SRR13535288 SRR13535290 SRR13535292
## 3107284 2321609 3701956 7929174 6330905 3686532
Create DDS objects
# Create DESeqDataSet object
dds <- get_DESeqDataSet_obj(counts_data, ~ treatment)
## [1] TRUE
## [1] TRUE
## [1] "DESeqDataSet object of length 55487 with 0 metadata columns"
## [1] "DESeqDataSet object of length 15188 with 0 metadata columns"
colData(dds)
## DataFrame with 6 rows and 25 columns
## Assay Type AvgSpotLen Bases BioProject BioSample Bytes Center Name Consent DATASTORE filetype DATASTORE provider DATASTORE region Experiment treatment GEO_Accession (exp) Instrument LibraryLayout LibrarySelection LibrarySource Organism Platform label ReleaseDate Sample Name source_name SRA Study
## <character> <numeric> <numeric> <character> <character> <numeric> <character> <character> <character> <character> <character> <character> <factor> <character> <character> <character> <character> <character> <character> <character> <factor> <POSIXct> <character> <character> <character>
## SRR13535276 RNA-Seq 300 8225466000 PRJNA694971 SAMN17588686 3252113587 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.us-east-1 SRX9943360 A GSM5043430 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043430 C2C12 proliferating myoblasts SRP303354
## SRR13535278 RNA-Seq 300 9203426700 PRJNA694971 SAMN17588684 3619152333 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.us-east-1 SRX9943362 A GSM5043433 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043433 C2C12 proliferating myoblasts SRP303354
## SRR13535280 RNA-Seq 300 9323939700 PRJNA694971 SAMN17588682 3735905901 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.us-east-1 SRX9943364 A GSM5043436 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043436 C2C12 proliferating myoblasts SRP303354
## SRR13535288 RNA-Seq 300 12863728500 PRJNA694971 SAMN17587373 5128876770 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.us-east-1 SRX9943372 C GSM5043450 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space with gravity 2021-09-09 GSM5043450 C2C12 proliferating myoblasts SRP303354
## SRR13535290 RNA-Seq 300 12849825300 PRJNA694971 SAMN17587371 5136077921 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.us-east-1 SRX9943374 C GSM5043454 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space with gravity 2021-09-09 GSM5043454 C2C12 proliferating myoblasts SRP303354
## SRR13535292 RNA-Seq 300 10569142200 PRJNA694971 SAMN17587369 4229018065 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.us-east-1 SRX9943376 C GSM5043457 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space with gravity 2021-09-09 GSM5043457 C2C12 proliferating myoblasts SRP303354
Sample-to-sample comparisons
# Transform data (blinded rlog)
rld <- get_transformed_data(dds)
PCA plot
pca <- rld$pca
pca_df <- cbind(as.data.frame(colData(dds)) %>% rownames_to_column(var = 'name'), pca$x)
summary(pca)
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6
## Standard deviation 30.6369 29.5690 26.7750 23.5645 20.988 9.01e-14
## Proportion of Variance 0.2662 0.2480 0.2033 0.1575 0.125 0.00e+00
## Cumulative Proportion 0.2662 0.5142 0.7176 0.8750 1.000 1.00e+00
ggplot(pca_df, aes(x = PC1, y = PC2, color = label)) +
geom_point() +
geom_text(aes(label = name), position = position_nudge(y = -2), show.legend = F, size = 3) +
scale_color_manual(values = colors_default) +
scale_x_continuous(expand = c(0.2, 0))
Correlation heatmap
pheatmap(
cor(rld$matrix),
annotation_col = as.data.frame(colData(dds)) %>% select(label),
color = brewer.pal(8, 'YlOrRd')
)
Wald test results
# DE analysis using Wald test
dds_full <- DESeq(dds)
colData(dds_full)
## DataFrame with 6 rows and 26 columns
## Assay Type AvgSpotLen Bases BioProject BioSample Bytes Center Name Consent DATASTORE filetype DATASTORE provider DATASTORE region Experiment treatment GEO_Accession (exp) Instrument LibraryLayout LibrarySelection LibrarySource Organism Platform label ReleaseDate Sample Name source_name SRA Study sizeFactor
## <character> <numeric> <numeric> <character> <character> <numeric> <character> <character> <character> <character> <character> <character> <factor> <character> <character> <character> <character> <character> <character> <character> <factor> <POSIXct> <character> <character> <character> <numeric>
## SRR13535276 RNA-Seq 300 8225466000 PRJNA694971 SAMN17588686 3252113587 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.us-east-1 SRX9943360 A GSM5043430 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043430 C2C12 proliferating myoblasts SRP303354 0.679517795445061
## SRR13535278 RNA-Seq 300 9203426700 PRJNA694971 SAMN17588684 3619152333 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.us-east-1 SRX9943362 A GSM5043433 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043433 C2C12 proliferating myoblasts SRP303354 0.949141263638101
## SRR13535280 RNA-Seq 300 9323939700 PRJNA694971 SAMN17588682 3735905901 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.us-east-1 SRX9943364 A GSM5043436 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043436 C2C12 proliferating myoblasts SRP303354 0.775037235318696
## SRR13535288 RNA-Seq 300 12863728500 PRJNA694971 SAMN17587373 5128876770 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.us-east-1 SRX9943372 C GSM5043450 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space with gravity 2021-09-09 GSM5043450 C2C12 proliferating myoblasts SRP303354 1.90346170413397
## SRR13535290 RNA-Seq 300 12849825300 PRJNA694971 SAMN17587371 5136077921 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.us-east-1 SRX9943374 C GSM5043454 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space with gravity 2021-09-09 GSM5043454 C2C12 proliferating myoblasts SRP303354 1.29343465761628
## SRR13535292 RNA-Seq 300 10569142200 PRJNA694971 SAMN17587369 4229018065 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.us-east-1 SRX9943376 C GSM5043457 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space with gravity 2021-09-09 GSM5043457 C2C12 proliferating myoblasts SRP303354 0.820640543694503
# Wald test results
res <- results(
dds_full,
contrast = c('treatment', condition, control),
alpha = pval_cutoff
)
res
## log2 fold change (MLE): treatment A vs C
## Wald test p-value: treatment A vs C
## DataFrame with 15188 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000098104 5.46467580943419 1.19222272527557 0.980392825585785 1.21606635030526 0.223959647695148 NA
## ENSMUSG00000103922 2.19054963487128 -0.26440015750769 2.00489254415536 -0.13187747057989 0.895081208841364 NA
## ENSMUSG00000033845 170.961551920931 -0.410659544303146 0.542058807911242 -0.757592235952355 0.448695129575756 0.897546598917915
## ENSMUSG00000102275 2.42482713116482 0.445772822746135 1.59993032331677 0.278620147546185 0.780536349253174 NA
## ENSMUSG00000025903 145.031917097888 -0.144158717997538 0.305940505671955 -0.471198534763856 0.637498964963172 0.941448632245291
## ... ... ... ... ... ... ...
## ENSMUSG00000061654 89.8396453928715 -5.63949816718769 3.75874932034961 -1.50036559678397 NA NA
## ENSMUSG00000079834 57.8910348496526 -0.191889534789484 0.531060354122093 -0.361332818953696 0.717850662655292 0.959205276501084
## ENSMUSG00000095041 282.407505680812 -0.142150831575804 0.680850436541708 -0.208784226236002 0.834616685616294 0.980565720274868
## ENSMUSG00000063897 38.5880238283551 0.249798899406184 0.436318288783867 0.572515307809899 0.566972918209039 0.928149881015563
## ENSMUSG00000095742 8.84404223926087 2.66871532821583 0.872879538070249 3.05736955882342 0.00223288800281555 0.13808205749417
mcols(res)
## DataFrame with 6 rows and 2 columns
## type description
## <character> <character>
## baseMean intermediate mean of normalized counts for all samples
## log2FoldChange results log2 fold change (MLE): treatment A vs C
## lfcSE results standard error: treatment A vs C
## stat results Wald statistic: treatment A vs C
## pvalue results Wald test p-value: treatment A vs C
## padj results BH adjusted p-values
summary(res)
##
## out of 15188 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 9, 0.059%
## LFC < 0 (down) : 72, 0.47%
## outliers [1] : 179, 1.2%
## low counts [2] : 3527, 23%
## (mean count < 8)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
plotDispEsts(dds_full)
Summary details
# Upregulated genes (LFC > 0)
res_sig_df %>% filter(log2FoldChange > 0)
# Downregulated genes (LFC < 0)
res_sig_df %>% filter(log2FoldChange < 0)
# Outliers (pvalue and padj are NA)
res[which(is.na(res$pvalue)), ]
## log2 fold change (MLE): treatment A vs C
## Wald test p-value: treatment A vs C
## DataFrame with 179 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000103509 9.61464501255551 -4.46237357619251 2.56029953073349 -1.74291075033479 NA NA
## ENSMUSG00000079554 38.1513589616965 -6.55179021966103 2.04362077425889 -3.20597162750854 NA NA
## ENSMUSG00000085842 32.4579801015249 3.9244091185441 2.2620877809193 1.73486155207878 NA NA
## ENSMUSG00000103553 13.011590868448 -4.72236490360827 2.9074692870391 -1.62421832782881 NA NA
## ENSMUSG00000102425 25.5983581230653 6.42741446982686 2.37852118878299 2.70227337058769 NA NA
## ... ... ... ... ... ... ...
## ENSMUSG00000024867 25.1587588691625 -1.33805691056957 1.33535206509993 -1.00202556729445 NA NA
## ENSMUSG00000117704 65.4156140694741 -4.86080355210405 1.98458148895025 -2.44928393173474 NA NA
## ENSMUSG00000025089 56.0189687241583 -1.68541394912476 1.37461618678161 -1.22609784849895 NA NA
## ENSMUSG00000048029 24.8195716674746 -6.69368791055847 3.9082810108819 -1.71269360926738 NA NA
## ENSMUSG00000061654 89.8396453928715 -5.63949816718769 3.75874932034961 -1.50036559678397 NA NA
# Low counts (only padj is NA)
res[which(is.na(res$padj) & !is.na(res$pvalue)), ]
## log2 fold change (MLE): treatment A vs C
## Wald test p-value: treatment A vs C
## DataFrame with 3527 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000098104 5.46467580943419 1.19222272527557 0.980392825585785 1.21606635030526 0.223959647695148 NA
## ENSMUSG00000103922 2.19054963487128 -0.26440015750769 2.00489254415536 -0.13187747057989 0.895081208841364 NA
## ENSMUSG00000102275 2.42482713116482 0.445772822746135 1.59993032331677 0.278620147546185 0.780536349253174 NA
## ENSMUSG00000103280 3.07359630335353 -0.631912445629046 1.2048909125565 -0.524456147061706 0.599961313256629 NA
## ENSMUSG00000033740 2.71686422103861 -2.68143373680077 2.56608325087095 -1.0449519655649 0.296045170568551 NA
## ... ... ... ... ... ... ...
## ENSMUSG00000064342 5.31912308829307 -0.322504137069612 1.2944673204709 -0.249140423994862 0.803252161252163 NA
## ENSMUSG00000064344 5.55025123689903 -0.733353272504932 1.32945753892904 -0.551618424079714 0.581209810777902 NA
## ENSMUSG00000064349 4.21295092586664 -0.0813580581645715 1.11002866545836 -0.0732936551066244 0.941572440613444 NA
## ENSMUSG00000064358 2.53259528400106 1.66589014336128 1.34176983185559 1.24156178191713 0.214398289974574 NA
## ENSMUSG00000064369 6.6742783733321 0.86939315845224 0.980303987049145 0.886860779857927 0.375153859560243 NA
Shrunken LFC results
plotMA(res)
# Shrunken LFC results
res_shrunken <- lfcShrink(
dds_full,
coef = str_c('treatment_', condition, '_vs_', control),
type = 'apeglm'
)
res_shrunken
## log2 fold change (MAP): treatment A vs C
## Wald test p-value: treatment A vs C
## DataFrame with 15188 rows and 5 columns
## baseMean log2FoldChange lfcSE pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000098104 5.46467580943419 0.0448465000467892 0.196771185236823 0.223959647695148 NA
## ENSMUSG00000103922 2.19054963487128 -0.00240059215008312 0.191823311959347 0.895081208841364 NA
## ENSMUSG00000033845 170.961551920931 -0.0468361261370054 0.188929482787428 0.448695129575756 0.897546598917915
## ENSMUSG00000102275 2.42482713116482 0.00639198418228839 0.191481742427142 0.780536349253174 NA
## ENSMUSG00000025903 145.031917097888 -0.041440107418986 0.167089292710563 0.637498964963172 0.941448632245291
## ... ... ... ... ... ...
## ENSMUSG00000061654 89.8396453928715 -0.00740276489536808 0.192885045874907 NA NA
## ENSMUSG00000079834 57.8910348496526 -0.0221472788783834 0.182797420790909 0.717850662655292 0.959205276501084
## ENSMUSG00000095041 282.407505680812 -0.0102447131743499 0.185774300288715 0.834616685616294 0.980565720274868
## ENSMUSG00000063897 38.5880238283551 0.0416776527425669 0.18152022206408 0.566972918209039 0.928149881015563
## ENSMUSG00000095742 8.84404223926087 1.8996514517983 1.10322539468332 0.00223288800281555 0.13808205749417
plotMA(res_shrunken)
mcols(res_shrunken)
## DataFrame with 5 rows and 2 columns
## type description
## <character> <character>
## baseMean intermediate mean of normalized counts for all samples
## log2FoldChange results log2 fold change (MAP): treatment A vs C
## lfcSE results posterior SD: treatment A vs C
## pvalue results Wald test p-value: treatment A vs C
## padj results BH adjusted p-values
summary(res_shrunken, alpha = pval_cutoff)
##
## out of 15188 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 9, 0.059%
## LFC < 0 (down) : 72, 0.47%
## outliers [1] : 179, 1.2%
## low counts [2] : 3527, 23%
## (mean count < 8)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
Summary details
# Upregulated genes (LFC > 0)
res_shrunken_sig_df %>% filter(log2FoldChange > 0)
# Downregulated genes (LFC < 0)
res_shrunken_sig_df %>% filter(log2FoldChange < 0)
# Outliers (pvalue and padj are NA)
res_shrunken[which(is.na(res_shrunken$pvalue)), ]
## log2 fold change (MAP): treatment A vs C
## Wald test p-value: treatment A vs C
## DataFrame with 179 rows and 5 columns
## baseMean log2FoldChange lfcSE pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000103509 9.61464501255551 -0.0173337016313202 0.193722511383804 NA NA
## ENSMUSG00000079554 38.1513589616965 -0.0340687331700345 0.197165077497607 NA NA
## ENSMUSG00000085842 32.4579801015249 0.0191760352566012 0.193937547808487 NA NA
## ENSMUSG00000103553 13.011590868448 -0.0129806187252493 0.193266149925685 NA NA
## ENSMUSG00000102425 25.5983581230653 0.0226261282085071 0.194638666145371 NA NA
## ... ... ... ... ... ...
## ENSMUSG00000024867 25.1587588691625 -0.025926542768652 0.193588517717408 NA NA
## ENSMUSG00000117704 65.4156140694741 -0.0268054351155857 0.195361376111712 NA NA
## ENSMUSG00000025089 56.0189687241583 -0.0297509352312334 0.19468689987549 NA NA
## ENSMUSG00000048029 24.8195716674746 -0.00752951146424761 0.192900745867621 NA NA
## ENSMUSG00000061654 89.8396453928715 -0.00740276489536808 0.192885045874907 NA NA
# Low counts (only padj is NA)
res_shrunken[which(is.na(res_shrunken$padj) & !is.na(res_shrunken$pvalue)), ]
## log2 fold change (MAP): treatment A vs C
## Wald test p-value: treatment A vs C
## DataFrame with 3527 rows and 5 columns
## baseMean log2FoldChange lfcSE pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000098104 5.46467580943419 0.0448465000467892 0.196771185236823 0.223959647695148 NA
## ENSMUSG00000103922 2.19054963487128 -0.00240059215008312 0.191823311959347 0.895081208841364 NA
## ENSMUSG00000102275 2.42482713116482 0.00639198418228839 0.191481742427142 0.780536349253174 NA
## ENSMUSG00000103280 3.07359630335353 -0.0159916365124215 0.191196494106891 0.599961313256629 NA
## ENSMUSG00000033740 2.71686422103861 -0.0134170705778806 0.193043718124969 0.296045170568551 NA
## ... ... ... ... ... ...
## ENSMUSG00000064342 5.31912308829307 -0.00681405379806286 0.190793669449769 0.803252161252163 NA
## ENSMUSG00000064344 5.55025123689903 -0.014632685762997 0.191601765157723 0.581209810777902 NA
## ENSMUSG00000064349 4.21295092586664 -0.00230977701249731 0.189870502471388 0.941572440613444 NA
## ENSMUSG00000064358 2.53259528400106 0.0339351464032784 0.19522701121723 0.214398289974574 NA
## ENSMUSG00000064369 6.6742783733321 0.032470491443182 0.193120576279053 0.375153859560243 NA
Visualizing results
Heatmaps
# Plot normalized counts (z-scores)
pheatmap(counts_sig_norm[2:7],
color = brewer.pal(8, 'YlOrRd'),
cluster_rows = T,
show_rownames = F,
annotation_col = as.data.frame(colData(dds)) %>% select(label),
border_color = NA,
fontsize = 10,
scale = 'row',
fontsize_row = 10,
height = 20)
# Plot log-transformed counts
pheatmap(counts_sig_log[2:7],
color = rev(brewer.pal(8, 'RdYlBu')),
cluster_rows = T,
show_rownames = F,
annotation_col = as.data.frame(colData(dds)) %>% select(label),
border_color = NA,
fontsize = 10,
fontsize_row = 10,
height = 20)
# Plot log-transformed counts (top 24 DE genes)
pheatmap((counts_sig_log %>% filter(ensembl_gene_id %in% res_sig_df$ensembl_gene_id[1:24]))[2:7],
color = rev(brewer.pal(8, 'RdYlBu')),
cluster_rows = T,
show_rownames = F,
annotation_col = as.data.frame(colData(dds)) %>% select(label),
fontsize = 10,
fontsize_row = 10,
height = 20)
Volcano plots
# Unshrunken LFC
res_df %>%
mutate(
sig_threshold = if_else(
padj < pval_cutoff & abs(log2FoldChange) >= lfc_cutoff,
if_else(log2FoldChange > 0, 'DE-up', 'DE-down'),
'non-DE'
)
) %>%
filter(!is.na(sig_threshold)) %>%
ggplot() +
geom_point(aes(x = log2FoldChange, y = -log10(padj), colour = sig_threshold)) +
scale_color_manual(values = c('blue', 'red', 'gray')) +
xlab('log2 fold change') +
ylab('-log10 adjusted p-value')
# Shrunken LFC
res_shrunken_df %>%
mutate(
sig_threshold = if_else(
padj < pval_cutoff & abs(log2FoldChange) >= lfc_cutoff,
if_else(log2FoldChange > 0, 'DE-up', 'DE-down'),
'non-DE'
)
) %>%
filter(!is.na(sig_threshold)) %>%
ggplot() +
geom_point(aes(x = log2FoldChange, y = -log10(padj), colour = sig_threshold)) +
scale_color_manual(values = c('blue', 'red', 'gray')) +
xlab('log2 fold change') +
ylab('-log10 adjusted p-value')
GSEA (all)
Hallmark genesets
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_h) %>% plot_enrichment_table(rank_lfc, mm_h)
# Wald stat
get_fgsea_res(rank_stat, mm_h) %>% plot_enrichment_table(rank_stat, mm_h)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_h) %>% plot_enrichment_table(rank_pval, mm_h)
GO biological process
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_bp) %>% plot_enrichment_table(rank_lfc, mm_c5_bp)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_bp) %>% plot_enrichment_table(rank_stat, mm_c5_bp)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_bp) %>% plot_enrichment_table(rank_pval, mm_c5_bp)
GO cellular component
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_cc) %>% plot_enrichment_table(rank_lfc, mm_c5_cc)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_cc) %>% plot_enrichment_table(rank_stat, mm_c5_cc)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_cc) %>% plot_enrichment_table(rank_pval, mm_c5_cc)
GO molecular function
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_mf) %>% plot_enrichment_table(rank_lfc, mm_c5_mf)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_mf) %>% plot_enrichment_table(rank_stat, mm_c5_mf)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_mf) %>% plot_enrichment_table(rank_pval, mm_c5_mf)
GSEA (DE)
Hallmark genesets
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_h) %>% plot_enrichment_table(rank_lfc, mm_h)
# Wald stat
get_fgsea_res(rank_stat, mm_h) %>% plot_enrichment_table(rank_stat, mm_h)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_h) %>% plot_enrichment_table(rank_pval, mm_h)
GO biological process
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_bp) %>% plot_enrichment_table(rank_lfc, mm_c5_bp)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_bp) %>% plot_enrichment_table(rank_stat, mm_c5_bp)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_bp) %>% plot_enrichment_table(rank_pval, mm_c5_bp)
GO cellular component
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_cc) %>% plot_enrichment_table(rank_lfc, mm_c5_cc)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_cc) %>% plot_enrichment_table(rank_stat, mm_c5_cc)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_cc) %>% plot_enrichment_table(rank_pval, mm_c5_cc)
GO molecular function
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_mf) %>% plot_enrichment_table(rank_lfc, mm_c5_mf)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_mf) %>% plot_enrichment_table(rank_stat, mm_c5_mf)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_mf) %>% plot_enrichment_table(rank_pval, mm_c5_mf)
System Info
sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Sierra 10.12.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] fgsea_1.12.0 Rcpp_1.0.3 RColorBrewer_1.1-2 pheatmap_1.0.12 DESeq2_1.26.0 SummarizedExperiment_1.16.1 DelayedArray_0.12.3 BiocParallel_1.20.1 matrixStats_0.57.0 Biobase_2.46.0 GenomicRanges_1.38.0 GenomeInfoDb_1.22.1 IRanges_2.20.2 S4Vectors_0.24.4 BiocGenerics_0.32.0 scales_1.1.1 forcats_0.4.0 stringr_1.4.0 dplyr_1.0.2 purrr_0.3.3 readr_1.3.1 tidyr_1.0.0 tibble_3.1.0 ggplot2_3.3.3 tidyverse_1.2.1
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.4-1 ellipsis_0.3.0 htmlTable_1.13.3 XVector_0.26.0 base64enc_0.1-3 rstudioapi_0.10 farver_2.1.0 bit64_0.9-7 mvtnorm_1.1-1 apeglm_1.8.0 AnnotationDbi_1.48.0 fansi_0.4.0 lubridate_1.7.4 xml2_1.2.2 splines_3.6.3 geneplotter_1.64.0 knitr_1.25 Formula_1.2-3 jsonlite_1.6 broom_0.7.5 annotate_1.64.0 cluster_2.1.0 png_0.1-7 compiler_3.6.3 httr_1.4.1 backports_1.1.5 assertthat_0.2.1 Matrix_1.2-18 cli_1.1.0 acepack_1.4.1 htmltools_0.5.1.1 tools_3.6.3 coda_0.19-3 gtable_0.3.0 glue_1.4.2 GenomeInfoDbData_1.2.2 fastmatch_1.1-0 bbmle_1.0.23.1 cellranger_1.1.0 jquerylib_0.1.3 vctrs_0.3.4 xfun_0.22 rvest_0.3.5 lifecycle_0.2.0 XML_3.99-0.3 MASS_7.3-51.5 zlibbioc_1.32.0 hms_0.5.2 yaml_2.2.0 memoise_1.1.0 gridExtra_2.3 emdbook_1.3.12 sass_0.3.1 bdsmatrix_1.3-4 rpart_4.1-15 latticeExtra_0.6-29 stringi_1.4.3 RSQLite_2.2.1 genefilter_1.68.0 checkmate_1.9.4 rlang_0.4.8 pkgconfig_2.0.3 bitops_1.0-6 evaluate_0.14 lattice_0.20-38 labeling_0.3 htmlwidgets_1.5.1 bit_1.1-15.1 tidyselect_1.1.0 plyr_1.8.4 magrittr_1.5 R6_2.4.0 generics_0.0.2 Hmisc_4.3-0 DBI_1.1.0 pillar_1.5.1 haven_2.2.0 foreign_0.8-75 withr_2.1.2 survival_3.1-8 RCurl_1.95-4.12 nnet_7.3-12 modelr_0.1.5 crayon_1.3.4 utf8_1.1.4 rmarkdown_2.7 jpeg_0.1-8.1 locfit_1.5-9.4 grid_3.6.3 readxl_1.3.1 data.table_1.13.6 blob_1.2.1 digest_0.6.27 xtable_1.8-4 numDeriv_2016.8-1.1 munsell_0.5.0 bslib_0.2.4